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Machine Learning Data Associate Jobs (NOW HIRING)

Extend

Senior Machine Learning Data Scientist

$135K - $165K/yr

The Fraud & Machine Learning team is the secret sauce behind Extend's post-purchase protection platform. As a Senior ML Data Scientist, you will own the development of cutting-edge machine learning ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning data pipelines. * Design tests for machine ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning data pipelines. * Design tests for machine ...

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Machine Learning Data Associate information

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How much do machine learning data associate jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for machine learning data associate in the United States is $18.74, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $19.95 per hour, depending on experience, location, and employer.

What is a machine learning data associate?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data associate?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

How does a machine learning data associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

How do I become a machine learning data associate?

To become a machine learning data associate, candidates typically need a high school diploma or equivalent, with some roles preferring a bachelor's degree in computer science, data science, or related fields. Relevant skills include data annotation, understanding of machine learning concepts, and proficiency with tools like Excel, SQL, or data labeling platforms. Gaining experience through internships or certifications can improve job prospects in this field.

Is a Machine Learning Data Associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Compensation and job satisfaction vary depending on the employer and location, but it generally provides a solid foundation for a career in machine learning or data analysis.

What cities are hiring for Machine Learning Data Associate jobs?

Cities with the most Machine Learning Data Associate job openings:

What states have the most Machine Learning Data Associate jobs?

States with the most job openings for Machine Learning Data Associate jobs include:

What are popular job titles related to Machine Learning Data Associate jobs?

For Machine Learning Data Associate jobs, the most frequently searched job titles are:

Machine Learning Data Associate jobs near you
Infographic showing various Machine Learning Data Associate job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $38,974 per year, or $18.7 per hour.

Machine Learning Data Engineer (DataOps), Materra

Mountain View, CA • On-site

X Development, LLC
See more jobs at X Development, LLCexternal

$134K - $161K/yr

Other

Re-posted 8 days ago

Job description

Software Engineering Mountain View, CA About the team

Materra is on a mission to radically reduce global waste and move to a true circular economy. The team has developed technology that identifies waste material at the molecular level—starting with plastics. Materra works with industry partners to improve the way recycling centers process plastics using AI and robotics, to make recycling more affordable and scalable.

About the Role

We are looking for a Machine Learning Data Engineer (DataOps) to build and unify the data infrastructure that powers our model training pipelines. In this role, you will lead the effort to consolidate fragmented data sources into a cohesive, high-quality data foundation.

Your primary focus will be designing automated ingestion pipelines, establishing data quality validation frameworks, and managing dataset versioning to support our machine learning training loops. You will bridge the gap between operations, remote annotation teams, and machine learning engineers to ensure our models are trained on reliable, well-structured data.

Key Responsibilities
  • Architect and build automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data pipelines to aggregate, clean, and harmonize data from disparate sources, databases, and operational ingestion flows.
  • Implement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection to catch corrupt or mislabeled data early.
  • Standardize and integrate third-party annotation workflows and remote labeling feeds into unified datasets ready for model training.
  • Design and maintain dataset versioning and storage systems to allow reproducible machine learning experiments and seamless data retrieval.
  • Collaborate with machine learning engineers and operations teams to translate raw material, form factor, and sensor metadata into structured training features.
Requirements
  • Education: Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
  • Data Engineering & Architecture: 3+ years experience building scalable data pipelines, managing relational and non-relational databases, and unifying fragmented data storage systems.
  • Modern Python Proficiency: Expertise in Python and data manipulation libraries (e.g., Pandas, NumPy, or SQL).
  • Data Quality & DataOps: Practical experience implementing automated data validation, quality control frameworks, and dataset versioning practices.
  • ML Data Lifecycle Understanding: Hands-on experience structuring datasets specifically for machine learning workflows, including handling annotations, metadata tracking, and training set curation.
Preferred Skills
  • Google Cloud Ecosystem: Hands-on experience with Google Cloud platform tools (e.g., BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines).
  • Workflow Orchestration: Experience managing pipelines using Google Cloud Composer or equivalent orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster).
  • Multimodal / Unstructured Data: Experience handling mixed data types, including image datasets, sensor metadata, and unstructured physical property records.
  • Annotation Platform Integration: Familiarity with data labeling platforms, human-in-the-loop workflows, or integrating third-party annotation APIs.
  • Validation & Versioning Tooling: Exposure to data quality and ML versioning tools (e.g., Great Expectations, DVC, or TFX/Data Validation).

The US base salary range for this full-time position is $166,000 - $244,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

An Equal Opportunity Workplace

At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

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